The automated detection of TV commercials has long been an area of great interest to different constituencies. Many consumers have wished for some way to mute or even skip entire commercials, particularly in programming that is recorded for time-shifted viewing. Numerous solutions for this need have been proposed and all run at the consumer device level and involve identifying commercials one at a time as they occur in the broadcast video stream being watched or recorded.
The other use of such automated content recognition technology is by those parties with a need to verify that specific TV commercials have actually been broadcast as contracted for on each of numerous channels, time slots and markets. In addition, other companies perform research and collect statistics on which companies are running which advertisements in which markets. This data can be for the companies' own use, or for resale. Meanwhile, other firms research commercials to develop more effective advertisements for clients. These types of analysis are usually done on a market-by-market level and require monitoring a large number of channels and markets.
For the past half-century, such commercial verification or collection procedures were manually performed by human beings during scheduled broadcast time(s), or by visually searching (fast forwarding, rewinding, etc.) a tape or other record of an earlier broadcast. As can be appreciated, waiting for a commercial to air (broadcast), setting up recording equipment to record a broadcast, and/or searching records of broadcast content to verify commercial content airing(s) can be time consuming, laborious, and costly undertakings. Therefore, there is an unmet need to optimize an automated process of detection and identification of commercial messages in live video streams.
Because consumers in nearly every market area now have access to cable and satellite systems simultaneously delivering hundreds of channels of programming, the ability to identify, verify, and track which commercials are being broadcast in which markets at any given time, presents a non-trivial computational challenge. The prior art in the area has been primarily focused on identifying when a single viewing device such as a TV set is presented with a commercial. Scaling approaches already known to those skilled in the art, to enable them to simultaneously monitor hundreds of steams of video in real time while keeping the costs of computation power and storage commercially reasonable remains an unmet need.